Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations953
Missing cells145
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory178.8 KiB
Average record size in memory192.1 B

Variable types

Text5
Numeric17
Categorical2

Alerts

in_apple_charts is highly overall correlated with in_spotify_chartsHigh correlation
in_apple_playlists is highly overall correlated with in_spotify_playlistsHigh correlation
in_deezer_charts is highly overall correlated with in_spotify_chartsHigh correlation
in_spotify_charts is highly overall correlated with in_apple_charts and 1 other fieldsHigh correlation
in_spotify_playlists is highly overall correlated with in_apple_playlists and 1 other fieldsHigh correlation
released_year is highly overall correlated with in_spotify_playlistsHigh correlation
in_shazam_charts has 50 (5.2%) missing valuesMissing
key has 95 (10.0%) missing valuesMissing
in_spotify_charts has 405 (42.5%) zerosZeros
in_apple_playlists has 23 (2.4%) zerosZeros
in_apple_charts has 100 (10.5%) zerosZeros
in_deezer_charts has 558 (58.6%) zerosZeros
acousticness_% has 60 (6.3%) zerosZeros
instrumentalness_% has 866 (90.9%) zerosZeros

Reproduction

Analysis started2024-09-15 22:20:07.677309
Analysis finished2024-09-15 22:20:36.579191
Duration28.9 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct943
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:36.839123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length123
Median length68
Mean length17.116474
Min length2

Characters and Unicode

Total characters16312
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique933 ?
Unique (%)97.9%

Sample

1st rowSeven (feat. Latto) (Explicit Ver.)
2nd rowLALA
3rd rowvampire
4th rowCruel Summer
5th rowWHERE SHE GOES
ValueCountFrequency (%)
95
 
3.2%
the 78
 
2.6%
feat 61
 
2.0%
with 46
 
1.5%
you 40
 
1.3%
me 39
 
1.3%
i 35
 
1.2%
a 26
 
0.9%
of 25
 
0.8%
love 24
 
0.8%
Other values (1499) 2530
84.4%
2024-09-15T22:20:37.306920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2052
 
12.6%
e 1355
 
8.3%
o 910
 
5.6%
a 890
 
5.5%
i 796
 
4.9%
r 697
 
4.3%
t 665
 
4.1%
n 628
 
3.8%
s 536
 
3.3%
l 457
 
2.8%
Other values (75) 7326
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2052
 
12.6%
e 1355
 
8.3%
o 910
 
5.6%
a 890
 
5.5%
i 796
 
4.9%
r 697
 
4.3%
t 665
 
4.1%
n 628
 
3.8%
s 536
 
3.3%
l 457
 
2.8%
Other values (75) 7326
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2052
 
12.6%
e 1355
 
8.3%
o 910
 
5.6%
a 890
 
5.5%
i 796
 
4.9%
r 697
 
4.3%
t 665
 
4.1%
n 628
 
3.8%
s 536
 
3.3%
l 457
 
2.8%
Other values (75) 7326
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2052
 
12.6%
e 1355
 
8.3%
o 910
 
5.6%
a 890
 
5.5%
i 796
 
4.9%
r 697
 
4.3%
t 665
 
4.1%
n 628
 
3.8%
s 536
 
3.3%
l 457
 
2.8%
Other values (75) 7326
44.9%
Distinct645
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:37.553973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length123
Median length69
Mean length16.285414
Min length1

Characters and Unicode

Total characters15520
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique541 ?
Unique (%)56.8%

Sample

1st rowLatto, Jung Kook
2nd rowMyke Towers
3rd rowOlivia Rodrigo
4th rowTaylor Swift
5th rowBad Bunny
ValueCountFrequency (%)
the 66
 
2.5%
bad 41
 
1.6%
bunny 40
 
1.5%
taylor 38
 
1.5%
swift 38
 
1.5%
weeknd 37
 
1.4%
sza 23
 
0.9%
kendrick 23
 
0.9%
lamar 23
 
0.9%
feid 21
 
0.8%
Other values (1042) 2240
86.5%
2024-09-15T22:20:37.955347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1639
 
10.6%
a 1339
 
8.6%
e 1196
 
7.7%
i 868
 
5.6%
n 836
 
5.4%
r 799
 
5.1%
o 730
 
4.7%
l 569
 
3.7%
, 529
 
3.4%
t 438
 
2.8%
Other values (67) 6577
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1639
 
10.6%
a 1339
 
8.6%
e 1196
 
7.7%
i 868
 
5.6%
n 836
 
5.4%
r 799
 
5.1%
o 730
 
4.7%
l 569
 
3.7%
, 529
 
3.4%
t 438
 
2.8%
Other values (67) 6577
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1639
 
10.6%
a 1339
 
8.6%
e 1196
 
7.7%
i 868
 
5.6%
n 836
 
5.4%
r 799
 
5.1%
o 730
 
4.7%
l 569
 
3.7%
, 529
 
3.4%
t 438
 
2.8%
Other values (67) 6577
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1639
 
10.6%
a 1339
 
8.6%
e 1196
 
7.7%
i 868
 
5.6%
n 836
 
5.4%
r 799
 
5.1%
o 730
 
4.7%
l 569
 
3.7%
, 529
 
3.4%
t 438
 
2.8%
Other values (67) 6577
42.4%

artist_count
Real number (ℝ)

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5561385
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:38.062711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89304419
Coefficient of variation (CV)0.57388477
Kurtosis10.366704
Mean1.5561385
Median Absolute Deviation (MAD)0
Skewness2.5440322
Sum1483
Variance0.79752793
MonotonicityNot monotonic
2024-09-15T22:20:38.163713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 587
61.6%
2 254
26.7%
3 85
 
8.9%
4 15
 
1.6%
5 5
 
0.5%
6 3
 
0.3%
8 2
 
0.2%
7 2
 
0.2%
ValueCountFrequency (%)
1 587
61.6%
2 254
26.7%
3 85
 
8.9%
4 15
 
1.6%
5 5
 
0.5%
6 3
 
0.3%
7 2
 
0.2%
8 2
 
0.2%
ValueCountFrequency (%)
8 2
 
0.2%
7 2
 
0.2%
6 3
 
0.3%
5 5
 
0.5%
4 15
 
1.6%
3 85
 
8.9%
2 254
26.7%
1 587
61.6%

released_year
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.2382
Minimum1930
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:38.286335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1930
5-th percentile1999
Q12020
median2022
Q32022
95-th percentile2023
Maximum2023
Range93
Interquartile range (IQR)2

Descriptive statistics

Standard deviation11.116218
Coefficient of variation (CV)0.0055078821
Kurtosis20.513396
Mean2018.2382
Median Absolute Deviation (MAD)1
Skewness-4.2921176
Sum1923381
Variance123.5703
MonotonicityNot monotonic
2024-09-15T22:20:38.417451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 402
42.2%
2023 175
18.4%
2021 119
 
12.5%
2020 37
 
3.9%
2019 36
 
3.8%
2017 23
 
2.4%
2016 18
 
1.9%
2013 13
 
1.4%
2014 13
 
1.4%
2015 11
 
1.2%
Other values (40) 106
 
11.1%
ValueCountFrequency (%)
1930 1
 
0.1%
1942 1
 
0.1%
1946 1
 
0.1%
1950 1
 
0.1%
1952 1
 
0.1%
1957 2
0.2%
1958 3
0.3%
1959 2
0.2%
1963 3
0.3%
1968 1
 
0.1%
ValueCountFrequency (%)
2023 175
18.4%
2022 402
42.2%
2021 119
 
12.5%
2020 37
 
3.9%
2019 36
 
3.8%
2018 10
 
1.0%
2017 23
 
2.4%
2016 18
 
1.9%
2015 11
 
1.2%
2014 13
 
1.4%

released_month
Real number (ℝ)

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0335782
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:38.525990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5664351
Coefficient of variation (CV)0.59109786
Kurtosis-1.1957936
Mean6.0335782
Median Absolute Deviation (MAD)3
Skewness0.18475844
Sum5750
Variance12.71946
MonotonicityNot monotonic
2024-09-15T22:20:38.628414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 134
14.1%
5 128
13.4%
3 86
9.0%
6 86
9.0%
11 80
8.4%
12 75
7.9%
10 73
7.7%
4 66
6.9%
7 62
6.5%
2 61
6.4%
Other values (2) 102
10.7%
ValueCountFrequency (%)
1 134
14.1%
2 61
6.4%
3 86
9.0%
4 66
6.9%
5 128
13.4%
6 86
9.0%
7 62
6.5%
8 46
 
4.8%
9 56
5.9%
10 73
7.7%
ValueCountFrequency (%)
12 75
7.9%
11 80
8.4%
10 73
7.7%
9 56
5.9%
8 46
 
4.8%
7 62
6.5%
6 86
9.0%
5 128
13.4%
4 66
6.9%
3 86
9.0%

released_day
Real number (ℝ)

Distinct31
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.930745
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:38.733825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median13
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.2019493
Coefficient of variation (CV)0.66054969
Kurtosis-1.2344145
Mean13.930745
Median Absolute Deviation (MAD)8
Skewness0.16410207
Sum13276
Variance84.675871
MonotonicityNot monotonic
2024-09-15T22:20:38.850476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 95
 
10.0%
21 44
 
4.6%
13 43
 
4.5%
24 40
 
4.2%
7 39
 
4.1%
2 39
 
4.1%
6 39
 
4.1%
4 39
 
4.1%
20 39
 
4.1%
10 37
 
3.9%
Other values (21) 499
52.4%
ValueCountFrequency (%)
1 95
10.0%
2 39
4.1%
3 32
 
3.4%
4 39
4.1%
5 25
 
2.6%
6 39
4.1%
7 39
4.1%
8 25
 
2.6%
9 36
 
3.8%
10 37
 
3.9%
ValueCountFrequency (%)
31 19
2.0%
30 22
2.3%
29 23
2.4%
28 21
2.2%
27 21
2.2%
26 13
 
1.4%
25 28
2.9%
24 40
4.2%
23 23
2.4%
22 33
3.5%

in_spotify_playlists
Real number (ℝ)

HIGH CORRELATION 

Distinct879
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5200.1249
Minimum31
Maximum52898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:38.975644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile268.2
Q1875
median2224
Q35542
95-th percentile22267.4
Maximum52898
Range52867
Interquartile range (IQR)4667

Descriptive statistics

Standard deviation7897.609
Coefficient of variation (CV)1.5187345
Kurtosis9.8761188
Mean5200.1249
Median Absolute Deviation (MAD)1595
Skewness2.9291262
Sum4955719
Variance62372228
MonotonicityNot monotonic
2024-09-15T22:20:39.111377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
356 3
 
0.3%
3006 3
 
0.3%
1112 3
 
0.3%
1473 3
 
0.3%
685 3
 
0.3%
811 3
 
0.3%
86 3
 
0.3%
896 3
 
0.3%
892 3
 
0.3%
1150 3
 
0.3%
Other values (869) 923
96.9%
ValueCountFrequency (%)
31 1
 
0.1%
34 1
 
0.1%
58 1
 
0.1%
67 1
 
0.1%
77 1
 
0.1%
86 3
0.3%
99 1
 
0.1%
105 1
 
0.1%
130 1
 
0.1%
134 1
 
0.1%
ValueCountFrequency (%)
52898 1
0.1%
51979 1
0.1%
50887 1
0.1%
49991 1
0.1%
44927 1
0.1%
43899 1
0.1%
43257 1
0.1%
42798 1
0.1%
41751 1
0.1%
41231 1
0.1%

in_spotify_charts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.009444
Minimum0
Maximum147
Zeros405
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:39.331279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q316
95-th percentile50
Maximum147
Range147
Interquartile range (IQR)16

Descriptive statistics

Standard deviation19.575992
Coefficient of variation (CV)1.6300498
Kurtosis8.5075814
Mean12.009444
Median Absolute Deviation (MAD)3
Skewness2.5804821
Sum11445
Variance383.21945
MonotonicityNot monotonic
2024-09-15T22:20:39.462594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 405
42.5%
4 48
 
5.0%
2 42
 
4.4%
6 36
 
3.8%
3 18
 
1.9%
8 17
 
1.8%
5 17
 
1.8%
12 16
 
1.7%
13 16
 
1.7%
1 16
 
1.7%
Other values (72) 322
33.8%
ValueCountFrequency (%)
0 405
42.5%
1 16
 
1.7%
2 42
 
4.4%
3 18
 
1.9%
4 48
 
5.0%
5 17
 
1.8%
6 36
 
3.8%
7 12
 
1.3%
8 17
 
1.8%
9 15
 
1.6%
ValueCountFrequency (%)
147 1
0.1%
130 1
0.1%
115 1
0.1%
113 1
0.1%
110 1
0.1%
104 1
0.1%
101 1
0.1%
100 1
0.1%
98 1
0.1%
91 1
0.1%
Distinct949
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:39.713379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length102
Median length9
Mean length9.09234
Min length4

Characters and Unicode

Total characters8665
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique945 ?
Unique (%)99.2%

Sample

1st row141381703
2nd row133716286
3rd row140003974
4th row800840817
5th row303236322
ValueCountFrequency (%)
1223481149 2
 
0.2%
723894473 2
 
0.2%
395591396 2
 
0.2%
156338624 2
 
0.2%
1047101291 1
 
0.1%
303236322 1
 
0.1%
170709584 1
 
0.1%
86444842 1
 
0.1%
141720999 1
 
0.1%
218320587 1
 
0.1%
Other values (939) 939
98.5%
2024-09-15T22:20:40.072737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1082
12.5%
3 949
11.0%
2 879
10.1%
6 855
9.9%
4 822
9.5%
5 819
9.5%
7 816
9.4%
9 816
9.4%
8 786
9.1%
0 753
8.7%
Other values (31) 88
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1082
12.5%
3 949
11.0%
2 879
10.1%
6 855
9.9%
4 822
9.5%
5 819
9.5%
7 816
9.4%
9 816
9.4%
8 786
9.1%
0 753
8.7%
Other values (31) 88
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1082
12.5%
3 949
11.0%
2 879
10.1%
6 855
9.9%
4 822
9.5%
5 819
9.5%
7 816
9.4%
9 816
9.4%
8 786
9.1%
0 753
8.7%
Other values (31) 88
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1082
12.5%
3 949
11.0%
2 879
10.1%
6 855
9.9%
4 822
9.5%
5 819
9.5%
7 816
9.4%
9 816
9.4%
8 786
9.1%
0 753
8.7%
Other values (31) 88
 
1.0%

in_apple_playlists
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct234
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.812172
Minimum0
Maximum672
Zeros23
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:40.204731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q113
median34
Q388
95-th percentile241.4
Maximum672
Range672
Interquartile range (IQR)75

Descriptive statistics

Standard deviation86.441493
Coefficient of variation (CV)1.2747194
Kurtosis7.9219753
Mean67.812172
Median Absolute Deviation (MAD)27
Skewness2.4739875
Sum64625
Variance7472.1317
MonotonicityNot monotonic
2024-09-15T22:20:40.339142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
2.4%
8 22
 
2.3%
16 20
 
2.1%
20 20
 
2.1%
10 20
 
2.1%
4 20
 
2.1%
7 20
 
2.1%
5 19
 
2.0%
3 17
 
1.8%
11 17
 
1.8%
Other values (224) 755
79.2%
ValueCountFrequency (%)
0 23
2.4%
1 16
1.7%
2 14
1.5%
3 17
1.8%
4 20
2.1%
5 19
2.0%
6 16
1.7%
7 20
2.1%
8 22
2.3%
9 12
1.3%
ValueCountFrequency (%)
672 1
0.1%
537 1
0.1%
533 1
0.1%
532 1
0.1%
492 1
0.1%
453 1
0.1%
440 1
0.1%
437 1
0.1%
433 1
0.1%
403 1
0.1%

in_apple_charts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct172
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.908709
Minimum0
Maximum275
Zeros100
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:40.467500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median38
Q387
95-th percentile142.4
Maximum275
Range275
Interquartile range (IQR)80

Descriptive statistics

Standard deviation50.630241
Coefficient of variation (CV)0.97537083
Kurtosis0.89052664
Mean51.908709
Median Absolute Deviation (MAD)35
Skewness1.0352492
Sum49469
Variance2563.4213
MonotonicityNot monotonic
2024-09-15T22:20:40.590234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 100
 
10.5%
1 40
 
4.2%
2 26
 
2.7%
3 24
 
2.5%
6 16
 
1.7%
5 15
 
1.6%
21 14
 
1.5%
10 13
 
1.4%
15 13
 
1.4%
9 13
 
1.4%
Other values (162) 679
71.2%
ValueCountFrequency (%)
0 100
10.5%
1 40
 
4.2%
2 26
 
2.7%
3 24
 
2.5%
4 10
 
1.0%
5 15
 
1.6%
6 16
 
1.7%
7 13
 
1.4%
8 10
 
1.0%
9 13
 
1.4%
ValueCountFrequency (%)
275 1
0.1%
266 1
0.1%
263 1
0.1%
227 1
0.1%
222 1
0.1%
215 1
0.1%
213 1
0.1%
212 1
0.1%
210 1
0.1%
207 2
0.2%
Distinct348
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:40.924656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.3179433
Min length1

Characters and Unicode

Total characters2209
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique219 ?
Unique (%)23.0%

Sample

1st row45
2nd row58
3rd row91
4th row125
5th row87
ValueCountFrequency (%)
0 24
 
2.5%
15 23
 
2.4%
13 20
 
2.1%
5 20
 
2.1%
12 18
 
1.9%
2 18
 
1.9%
6 18
 
1.9%
8 18
 
1.9%
3 17
 
1.8%
4 17
 
1.8%
Other values (338) 760
79.7%
2024-09-15T22:20:41.367568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 402
18.2%
2 267
12.1%
3 253
11.5%
5 233
10.5%
4 206
9.3%
0 177
8.0%
6 161
7.3%
8 157
 
7.1%
7 140
 
6.3%
9 134
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2209
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 402
18.2%
2 267
12.1%
3 253
11.5%
5 233
10.5%
4 206
9.3%
0 177
8.0%
6 161
7.3%
8 157
 
7.1%
7 140
 
6.3%
9 134
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2209
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 402
18.2%
2 267
12.1%
3 253
11.5%
5 233
10.5%
4 206
9.3%
0 177
8.0%
6 161
7.3%
8 157
 
7.1%
7 140
 
6.3%
9 134
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2209
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 402
18.2%
2 267
12.1%
3 253
11.5%
5 233
10.5%
4 206
9.3%
0 177
8.0%
6 161
7.3%
8 157
 
7.1%
7 140
 
6.3%
9 134
 
6.1%

in_deezer_charts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6663169
Minimum0
Maximum58
Zeros558
Zeros (%)58.6%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:41.490522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile15
Maximum58
Range58
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.0355989
Coefficient of variation (CV)2.2636465
Kurtosis19.021085
Mean2.6663169
Median Absolute Deviation (MAD)0
Skewness3.766095
Sum2541
Variance36.428455
MonotonicityNot monotonic
2024-09-15T22:20:41.609788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 558
58.6%
1 137
 
14.4%
2 48
 
5.0%
3 31
 
3.3%
6 18
 
1.9%
4 18
 
1.9%
5 18
 
1.9%
9 14
 
1.5%
11 11
 
1.2%
14 11
 
1.2%
Other values (24) 89
 
9.3%
ValueCountFrequency (%)
0 558
58.6%
1 137
 
14.4%
2 48
 
5.0%
3 31
 
3.3%
4 18
 
1.9%
5 18
 
1.9%
6 18
 
1.9%
7 7
 
0.7%
8 8
 
0.8%
9 14
 
1.5%
ValueCountFrequency (%)
58 1
 
0.1%
46 1
 
0.1%
45 1
 
0.1%
38 2
0.2%
37 2
0.2%
31 1
 
0.1%
29 1
 
0.1%
28 1
 
0.1%
26 3
0.3%
24 3
0.3%

in_shazam_charts
Text

MISSING 

Distinct198
Distinct (%)21.9%
Missing50
Missing (%)5.2%
Memory size7.6 KiB
2024-09-15T22:20:41.912556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.5548173
Min length1

Characters and Unicode

Total characters1404
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)12.6%

Sample

1st row826
2nd row382
3rd row949
4th row548
5th row425
ValueCountFrequency (%)
0 344
38.1%
1 73
 
8.1%
2 35
 
3.9%
3 21
 
2.3%
4 19
 
2.1%
5 15
 
1.7%
6 12
 
1.3%
10 11
 
1.2%
9 11
 
1.2%
7 10
 
1.1%
Other values (188) 352
39.0%
2024-09-15T22:20:42.340914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 408
29.1%
1 259
18.4%
2 152
 
10.8%
3 119
 
8.5%
4 100
 
7.1%
5 87
 
6.2%
6 82
 
5.8%
8 70
 
5.0%
9 60
 
4.3%
7 60
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 408
29.1%
1 259
18.4%
2 152
 
10.8%
3 119
 
8.5%
4 100
 
7.1%
5 87
 
6.2%
6 82
 
5.8%
8 70
 
5.0%
9 60
 
4.3%
7 60
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 408
29.1%
1 259
18.4%
2 152
 
10.8%
3 119
 
8.5%
4 100
 
7.1%
5 87
 
6.2%
6 82
 
5.8%
8 70
 
5.0%
9 60
 
4.3%
7 60
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 408
29.1%
1 259
18.4%
2 152
 
10.8%
3 119
 
8.5%
4 100
 
7.1%
5 87
 
6.2%
6 82
 
5.8%
8 70
 
5.0%
9 60
 
4.3%
7 60
 
4.3%

bpm
Real number (ℝ)

Distinct124
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.5404
Minimum65
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:42.471003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile81
Q1100
median121
Q3140
95-th percentile174
Maximum206
Range141
Interquartile range (IQR)40

Descriptive statistics

Standard deviation28.057802
Coefficient of variation (CV)0.22896777
Kurtosis-0.39902742
Mean122.5404
Median Absolute Deviation (MAD)21
Skewness0.41324555
Sum116781
Variance787.24023
MonotonicityNot monotonic
2024-09-15T22:20:42.604012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 39
 
4.1%
130 31
 
3.3%
140 31
 
3.3%
92 25
 
2.6%
110 24
 
2.5%
150 21
 
2.2%
90 21
 
2.2%
122 19
 
2.0%
105 19
 
2.0%
125 18
 
1.9%
Other values (114) 705
74.0%
ValueCountFrequency (%)
65 2
 
0.2%
67 1
 
0.1%
71 3
 
0.3%
72 3
 
0.3%
73 1
 
0.1%
74 1
 
0.1%
75 1
 
0.1%
76 2
 
0.2%
77 4
0.4%
78 9
0.9%
ValueCountFrequency (%)
206 2
0.2%
204 1
0.1%
202 2
0.2%
200 1
0.1%
198 1
0.1%
196 1
0.1%
192 1
0.1%
189 1
0.1%
188 1
0.1%
186 2
0.2%

key
Categorical

MISSING 

Distinct11
Distinct (%)1.3%
Missing95
Missing (%)10.0%
Memory size7.6 KiB
C#
120 
G
96 
G#
91 
F
89 
B
81 
Other values (6)
381 

Length

Max length2
Median length1
Mean length1.4358974
Min length1

Characters and Unicode

Total characters1232
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC#
3rd rowF
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
C# 120
12.6%
G 96
10.1%
G# 91
9.5%
F 89
9.3%
B 81
8.5%
D 81
8.5%
A 75
7.9%
F# 73
7.7%
E 62
6.5%
A# 57
6.0%
(Missing) 95
10.0%

Length

2024-09-15T22:20:42.731067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g 187
21.8%
f 162
18.9%
a 132
15.4%
c 120
14.0%
d 114
13.3%
b 81
9.4%
e 62
 
7.2%

Most occurring characters

ValueCountFrequency (%)
# 374
30.4%
G 187
15.2%
F 162
13.1%
A 132
 
10.7%
C 120
 
9.7%
D 114
 
9.3%
B 81
 
6.6%
E 62
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
# 374
30.4%
G 187
15.2%
F 162
13.1%
A 132
 
10.7%
C 120
 
9.7%
D 114
 
9.3%
B 81
 
6.6%
E 62
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
# 374
30.4%
G 187
15.2%
F 162
13.1%
A 132
 
10.7%
C 120
 
9.7%
D 114
 
9.3%
B 81
 
6.6%
E 62
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
# 374
30.4%
G 187
15.2%
F 162
13.1%
A 132
 
10.7%
C 120
 
9.7%
D 114
 
9.3%
B 81
 
6.6%
E 62
 
5.0%

mode
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
Major
550 
Minor
403 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4765
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMajor
2nd rowMajor
3rd rowMajor
4th rowMajor
5th rowMinor

Common Values

ValueCountFrequency (%)
Major 550
57.7%
Minor 403
42.3%

Length

2024-09-15T22:20:42.837456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T22:20:43.013617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
major 550
57.7%
minor 403
42.3%

Most occurring characters

ValueCountFrequency (%)
M 953
20.0%
r 953
20.0%
o 953
20.0%
j 550
11.5%
a 550
11.5%
i 403
8.5%
n 403
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 953
20.0%
r 953
20.0%
o 953
20.0%
j 550
11.5%
a 550
11.5%
i 403
8.5%
n 403
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 953
20.0%
r 953
20.0%
o 953
20.0%
j 550
11.5%
a 550
11.5%
i 403
8.5%
n 403
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 953
20.0%
r 953
20.0%
o 953
20.0%
j 550
11.5%
a 550
11.5%
i 403
8.5%
n 403
8.5%

danceability_%
Real number (ℝ)

Distinct72
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.96957
Minimum23
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:43.114286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile40.6
Q157
median69
Q378
95-th percentile89
Maximum96
Range73
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.63061
Coefficient of variation (CV)0.21846654
Kurtosis-0.33356575
Mean66.96957
Median Absolute Deviation (MAD)10
Skewness-0.43587813
Sum63822
Variance214.05475
MonotonicityNot monotonic
2024-09-15T22:20:43.251428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 43
 
4.5%
77 32
 
3.4%
80 31
 
3.3%
56 30
 
3.1%
74 29
 
3.0%
81 28
 
2.9%
73 27
 
2.8%
71 26
 
2.7%
78 26
 
2.7%
65 26
 
2.7%
Other values (62) 655
68.7%
ValueCountFrequency (%)
23 1
 
0.1%
24 1
 
0.1%
25 1
 
0.1%
27 1
 
0.1%
28 2
 
0.2%
29 1
 
0.1%
31 4
0.4%
32 2
 
0.2%
33 3
0.3%
34 7
0.7%
ValueCountFrequency (%)
96 1
 
0.1%
95 6
0.6%
94 1
 
0.1%
93 5
0.5%
92 10
1.0%
91 12
1.3%
90 9
0.9%
89 7
0.7%
88 7
0.7%
87 10
1.0%

valence_%
Real number (ℝ)

Distinct94
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.43127
Minimum4
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:43.390990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14
Q132
median51
Q370
95-th percentile90
Maximum97
Range93
Interquartile range (IQR)38

Descriptive statistics

Standard deviation23.480632
Coefficient of variation (CV)0.45654389
Kurtosis-0.93933627
Mean51.43127
Median Absolute Deviation (MAD)19
Skewness0.0082235369
Sum49014
Variance551.34007
MonotonicityNot monotonic
2024-09-15T22:20:43.525649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 21
 
2.2%
40 20
 
2.1%
59 18
 
1.9%
55 18
 
1.9%
53 18
 
1.9%
61 17
 
1.8%
49 16
 
1.7%
22 16
 
1.7%
42 15
 
1.6%
50 15
 
1.6%
Other values (84) 779
81.7%
ValueCountFrequency (%)
4 5
0.5%
5 2
 
0.2%
6 3
0.3%
7 5
0.5%
8 4
0.4%
9 2
 
0.2%
10 6
0.6%
11 6
0.6%
12 6
0.6%
13 5
0.5%
ValueCountFrequency (%)
97 5
 
0.5%
96 13
1.4%
95 1
 
0.1%
94 4
 
0.4%
93 4
 
0.4%
92 9
0.9%
91 7
0.7%
90 10
1.0%
89 6
0.6%
88 9
0.9%

energy_%
Real number (ℝ)

Distinct80
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.279119
Minimum9
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:43.656385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile34.6
Q153
median66
Q377
95-th percentile89
Maximum97
Range88
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.550526
Coefficient of variation (CV)0.25747904
Kurtosis-0.25998229
Mean64.279119
Median Absolute Deviation (MAD)12
Skewness-0.44639922
Sum61258
Variance273.91991
MonotonicityNot monotonic
2024-09-15T22:20:43.784103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 29
 
3.0%
62 28
 
2.9%
76 27
 
2.8%
66 25
 
2.6%
73 23
 
2.4%
68 23
 
2.4%
60 23
 
2.4%
80 22
 
2.3%
67 22
 
2.3%
79 22
 
2.3%
Other values (70) 709
74.4%
ValueCountFrequency (%)
9 1
 
0.1%
14 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
20 4
0.4%
23 1
 
0.1%
24 4
0.4%
25 3
0.3%
26 2
0.2%
27 3
0.3%
ValueCountFrequency (%)
97 2
 
0.2%
96 2
 
0.2%
95 1
 
0.1%
94 6
0.6%
93 3
 
0.3%
92 4
 
0.4%
91 9
0.9%
90 12
1.3%
89 14
1.5%
88 12
1.3%

acousticness_%
Real number (ℝ)

ZEROS 

Distinct98
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.057712
Minimum0
Maximum97
Zeros60
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:43.914767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median18
Q343
95-th percentile81.4
Maximum97
Range97
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.996077
Coefficient of variation (CV)0.96076405
Kurtosis-0.19208351
Mean27.057712
Median Absolute Deviation (MAD)15
Skewness0.9524617
Sum25786
Variance675.79604
MonotonicityNot monotonic
2024-09-15T22:20:44.046372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
 
6.3%
1 48
 
5.0%
4 35
 
3.7%
2 33
 
3.5%
3 30
 
3.1%
5 30
 
3.1%
9 29
 
3.0%
6 29
 
3.0%
11 24
 
2.5%
7 22
 
2.3%
Other values (88) 613
64.3%
ValueCountFrequency (%)
0 60
6.3%
1 48
5.0%
2 33
3.5%
3 30
3.1%
4 35
3.7%
5 30
3.1%
6 29
3.0%
7 22
 
2.3%
8 17
 
1.8%
9 29
3.0%
ValueCountFrequency (%)
97 2
 
0.2%
96 1
 
0.1%
95 1
 
0.1%
94 2
 
0.2%
93 2
 
0.2%
92 3
0.3%
91 5
0.5%
90 3
0.3%
89 2
 
0.2%
88 2
 
0.2%

instrumentalness_%
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5813221
Minimum0
Maximum91
Zeros866
Zeros (%)90.9%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:44.169124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum91
Range91
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.4097999
Coefficient of variation (CV)5.3182079
Kurtosis56.635596
Mean1.5813221
Median Absolute Deviation (MAD)0
Skewness7.1242172
Sum1507
Variance70.724735
MonotonicityNot monotonic
2024-09-15T22:20:44.286223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 866
90.9%
1 21
 
2.2%
2 7
 
0.7%
4 5
 
0.5%
3 4
 
0.4%
5 4
 
0.4%
18 3
 
0.3%
6 3
 
0.3%
9 3
 
0.3%
63 3
 
0.3%
Other values (29) 34
 
3.6%
ValueCountFrequency (%)
0 866
90.9%
1 21
 
2.2%
2 7
 
0.7%
3 4
 
0.4%
4 5
 
0.5%
5 4
 
0.4%
6 3
 
0.3%
8 2
 
0.2%
9 3
 
0.3%
10 2
 
0.2%
ValueCountFrequency (%)
91 1
 
0.1%
90 1
 
0.1%
83 1
 
0.1%
72 1
 
0.1%
63 3
0.3%
61 1
 
0.1%
51 2
0.2%
47 1
 
0.1%
46 1
 
0.1%
44 1
 
0.1%

liveness_%
Real number (ℝ)

Distinct68
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.213012
Minimum3
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:44.409774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q110
median12
Q324
95-th percentile44.4
Maximum97
Range94
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.711223
Coefficient of variation (CV)0.75282571
Kurtosis5.7143954
Mean18.213012
Median Absolute Deviation (MAD)4
Skewness2.10428
Sum17357
Variance187.99765
MonotonicityNot monotonic
2024-09-15T22:20:44.546070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 102
 
10.7%
9 93
 
9.8%
10 78
 
8.2%
12 72
 
7.6%
8 54
 
5.7%
13 47
 
4.9%
7 38
 
4.0%
15 36
 
3.8%
14 29
 
3.0%
6 26
 
2.7%
Other values (58) 378
39.7%
ValueCountFrequency (%)
3 4
 
0.4%
4 5
 
0.5%
5 16
 
1.7%
6 26
 
2.7%
7 38
 
4.0%
8 54
5.7%
9 93
9.8%
10 78
8.2%
11 102
10.7%
12 72
7.6%
ValueCountFrequency (%)
97 1
0.1%
92 1
0.1%
91 1
0.1%
90 1
0.1%
83 1
0.1%
80 2
0.2%
77 1
0.1%
72 2
0.2%
67 1
0.1%
66 2
0.2%

speechiness_%
Real number (ℝ)

Distinct48
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.131165
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-09-15T22:20:44.672277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median6
Q311
95-th percentile33
Maximum64
Range62
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.9128876
Coefficient of variation (CV)0.97845488
Kurtosis3.3744263
Mean10.131165
Median Absolute Deviation (MAD)2
Skewness1.9346683
Sum9655
Variance98.265341
MonotonicityNot monotonic
2024-09-15T22:20:44.899199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
4 175
18.4%
3 152
15.9%
5 130
13.6%
6 76
 
8.0%
8 52
 
5.5%
7 49
 
5.1%
9 37
 
3.9%
10 24
 
2.5%
11 22
 
2.3%
12 16
 
1.7%
Other values (38) 220
23.1%
ValueCountFrequency (%)
2 3
 
0.3%
3 152
15.9%
4 175
18.4%
5 130
13.6%
6 76
8.0%
7 49
 
5.1%
8 52
 
5.5%
9 37
 
3.9%
10 24
 
2.5%
11 22
 
2.3%
ValueCountFrequency (%)
64 1
 
0.1%
59 1
 
0.1%
49 1
 
0.1%
46 3
0.3%
45 2
0.2%
44 2
0.2%
43 1
 
0.1%
42 1
 
0.1%
41 1
 
0.1%
40 4
0.4%

Interactions

2024-09-15T22:20:34.348077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:08.218647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:09.887836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:11.490945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:13.045485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:14.745301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:16.331123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:18.042586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:19.753979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:21.351091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:23.033120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:24.644602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:26.399445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:28.041488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.546912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:31.178748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.713240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:34.444419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:08.319271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:09.983392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:11.586805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:13.143879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:14.845592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:16.529819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:18.140331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:19.850795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:21.450294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:23.133801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:24.846192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:26.495790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:28.134310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.640985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:31.272813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.813008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:34.532727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:08.410135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:10.067101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:11.673330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:13.233443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:14.935392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:16.623167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:18.231295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:19.938689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:21.537744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:23.223261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:24.936185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:26.580988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:28.219955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.727965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:31.356139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.898422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:34.621300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:08.501417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:10.154826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:11.762671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:13.324904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:15.026760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:16.715751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:18.323845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:20.044401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:21.630990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:23.316726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:25.030129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:26.672004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:28.306791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.816001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:31.444211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.987099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:34.714835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:08.660043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:10.247288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:11.855894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:13.419244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:15.121202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:16.812718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:18.420575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:20.149449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:21.725490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:23.411595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:25.126076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:26.764840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:28.397956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.909223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:31.534869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:33.077557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:34.806152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:08.752044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:10.335575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:11.947697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:13.511221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:15.209738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:16.906539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:18.513844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:20.252820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:21.818621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:23.505257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:25.222040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:26.854912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:28.485653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.998656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:31.624685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:33.167724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:34.903745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:08.850563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:10.429917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:12.044408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:13.701318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:15.306667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:17.005508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:18.615726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:20.348758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:21.917997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:23.603908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:25.321552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:26.948945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:28.580238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:30.093783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:31.720667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:33.262085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:35.000216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:08.950422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:10.524629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:12.141642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:13.800060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:15.403947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:17.105683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:18.715950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:20.445294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:22.111669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:23.706976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:25.439510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:27.046139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:28.673878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:30.188631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:31.817449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:33.358728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:35.091483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:09.043041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:10.613385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:12.228826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:13.889604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:15.493534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:17.199484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:18.808261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:20.532489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:22.202394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:23.798549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:25.544865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:27.134075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:28.761750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:30.278116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:31.907640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:33.446329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:35.183491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:09.140692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:10.704552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:12.321126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:13.986722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:15.588039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:17.296691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:18.906082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:20.626227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:22.295389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:23.896055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:25.642120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:27.226602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:28.851239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:30.368324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.000348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:33.538960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:35.280869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:09.238773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:10.799866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:12.414599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:14.082215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:15.686084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:17.396316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:19.005002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:20.721649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:22.393267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:23.994866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:25.742764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:27.321049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:28.942461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:30.463968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.096656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:33.632199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:35.375547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:09.339222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:10.894434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:12.513108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:14.180313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:15.784999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:17.496529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:19.197079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:20.818866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:22.491486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:24.094188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:25.840622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:27.418075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.036570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:30.559106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.190912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:33.825005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:35.464850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:09.430016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:11.059532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:12.601936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:14.268791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:15.875534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:17.587181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:19.289238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:20.906988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:22.582246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:24.187742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:25.936731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:27.504925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.121844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:30.738749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.279435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:33.913805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:35.549811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:09.518402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:11.143938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:12.687551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:14.355889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:15.964752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:17.676774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:19.379950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:20.993377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:22.669572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:24.276080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:26.026296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:27.687466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.203777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:30.829563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.362838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:33.999402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:35.640537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:09.610029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:11.231662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:12.776369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:14.448403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:16.056666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:17.767863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:19.471769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:21.082434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:22.761628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:24.368723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:26.119372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:27.776067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.289412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:30.917236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.452348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:34.085912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:35.725796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:09.699524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:11.314291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:12.863117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:14.537136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:16.145298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:17.856371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:19.562766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:21.168723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:22.848955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:24.457687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:26.208585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:27.861176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.372016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:31.000957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.536999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:34.170233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:35.819023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:09.791744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:11.401127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:12.950982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:14.631650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:16.236148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:17.947665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:19.655923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:21.258996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:22.940285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:24.550075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:26.304248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:27.949620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:29.456188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:31.088593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:32.624149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-15T22:20:34.257186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-15T22:20:45.008316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
acousticness_%artist_countbpmdanceability_%energy_%in_apple_chartsin_apple_playlistsin_deezer_chartsin_spotify_chartsin_spotify_playlistsinstrumentalness_%keyliveness_%modereleased_dayreleased_monthreleased_yearspeechiness_%valence_%
acousticness_%1.000-0.063-0.034-0.147-0.475-0.081-0.129-0.024-0.059-0.0870.0490.038-0.0280.0770.0090.0420.014-0.048-0.019
artist_count-0.0631.000-0.0490.2480.153-0.066-0.0210.0620.010-0.112-0.0750.0000.0380.064-0.0010.0330.1950.1770.129
bpm-0.034-0.0491.000-0.1240.0280.0390.0180.0370.0560.000-0.0400.0310.0000.000-0.037-0.0310.0330.0440.042
danceability_%-0.1470.248-0.1241.0000.148-0.0180.0010.1080.037-0.107-0.0740.064-0.0930.1180.054-0.0360.1970.2830.400
energy_%-0.4750.1530.0280.1481.0000.1080.0590.1370.101-0.026-0.0540.0150.0460.0910.054-0.0710.0810.0900.353
in_apple_charts-0.081-0.0660.039-0.0180.1081.0000.4470.4100.5210.262-0.0430.019-0.0070.000-0.013-0.009-0.022-0.1190.037
in_apple_playlists-0.129-0.0210.0180.0010.0590.4471.0000.3450.2050.781-0.0440.000-0.0300.0170.020-0.039-0.406-0.0770.020
in_deezer_charts-0.0240.0620.0370.1080.1370.4100.3451.0000.5890.210-0.0330.0510.0200.0240.058-0.0170.155-0.0300.126
in_spotify_charts-0.0590.0100.0560.0370.1010.5210.2050.5891.0000.128-0.0320.000-0.0400.0440.025-0.0310.146-0.0880.047
in_spotify_playlists-0.087-0.1120.000-0.107-0.0260.2620.7810.2100.1281.0000.0480.000-0.0400.000-0.037-0.041-0.660-0.062-0.077
instrumentalness_%0.049-0.075-0.040-0.074-0.054-0.043-0.044-0.033-0.0320.0481.0000.026-0.0330.000-0.0110.058-0.051-0.125-0.139
key0.0380.0000.0310.0640.0150.0190.0000.0510.0000.0000.0261.0000.0000.2770.0450.0000.0000.0250.073
liveness_%-0.0280.0380.000-0.0930.046-0.007-0.0300.020-0.040-0.040-0.0330.0001.0000.0000.005-0.0140.083-0.021-0.015
mode0.0770.0640.0000.1180.0910.0000.0170.0240.0440.0000.0000.2770.0001.0000.0850.0440.0650.0310.000
released_day0.009-0.001-0.0370.0540.054-0.0130.0200.0580.025-0.037-0.0110.0450.0050.0851.0000.1040.1080.0320.042
released_month0.0420.033-0.031-0.036-0.071-0.009-0.039-0.017-0.031-0.0410.0580.000-0.0140.0440.1041.000-0.1240.068-0.117
released_year0.0140.1950.0330.1970.081-0.022-0.4060.1550.146-0.660-0.0510.0000.0830.0650.108-0.1241.0000.1220.072
speechiness_%-0.0480.1770.0440.2830.090-0.119-0.077-0.030-0.088-0.062-0.1250.025-0.0210.0310.0320.0680.1221.0000.111
valence_%-0.0190.1290.0420.4000.3530.0370.0200.1260.047-0.077-0.1390.073-0.0150.0000.042-0.1170.0720.1111.000

Missing values

2024-09-15T22:20:35.976499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-15T22:20:36.278281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-15T22:20:36.527761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

track_nameartist(s)_nameartist_countreleased_yearreleased_monthreleased_dayin_spotify_playlistsin_spotify_chartsstreamsin_apple_playlistsin_apple_chartsin_deezer_playlistsin_deezer_chartsin_shazam_chartsbpmkeymodedanceability_%valence_%energy_%acousticness_%instrumentalness_%liveness_%speechiness_%
0Seven (feat. Latto) (Explicit Ver.)Latto, Jung Kook22023714553147141381703432634510826125BMajor80898331084
1LALAMyke Towers1202332314744813371628648126581438292C#Major71617470104
2vampireOlivia Rodrigo120236301397113140003974942079114949138FMajor513253170316
3Cruel SummerTaylor Swift12019823785810080084081711620712512548170AMajor5558721101115
4WHERE SHE GOESBad Bunny12023518313350303236322841338715425144AMinor6523801463116
5SprinterDave, Central Cee2202361218691183706234672138817946141C#Major926658190824
6Ella Baila SolaEslabon Armado, Peso Pluma22023316309050725980112342224313418148FMinor67837648083
7ColumbiaQuevedo1202377714435814937825893013194100FMajor672671370114
8fukumeanGunna1202351510968395217315602104811953130C#Minor852262120289
9La Bebe - RemixPeso Pluma, Yng Lvcas22023317295344553634067491106613339170DMinor815648210833
track_nameartist(s)_nameartist_countreleased_yearreleased_monthreleased_dayin_spotify_playlistsin_spotify_chartsstreamsin_apple_playlistsin_apple_chartsin_deezer_playlistsin_deezer_chartsin_shazam_chartsbpmkeymodedanceability_%valence_%energy_%acousticness_%instrumentalness_%liveness_%speechiness_%
943Privileged RappersDrake, 21 Savage220221141007011243640365300144FMajor936261001220
944The AstronautJin12022102848192034364681010015127125FMajor54227600143
945BackOutsideBoyzDrake12022114104509336753785200142FMinor854043403932
946Broke BoysDrake, 21 Savage220221141060010624921938500120DMajor641153102527
947The Great WarTaylor Swift1202210211274018138259016110096FMajor57557422084
948My Mind & MeSelena Gomez1202211395309147336361133710144AMajor60243957083
949Bigger Than The Whole SkyTaylor Swift1202210211180012187187040800166F#Major42724831126
950A Veces (feat. Feid)Feid, Paulo Londra220221135730735136832070092C#Major8081674086
951En La De EllaFeid, Sech, Jhayco320221020132001338956122926170097C#Major82677780125
952AloneBurna Boy120221147822960073912718321090EMinor613267150115